Towards Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models

The code and models for predicting IR spectra of polycyclic aromatic hydrocarbons using machine learning methods.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-19
DOI
https://doi.org/10.5281/zenodo.14894780
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Towards Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models

article en

Abstract

The code and models for predicting IR spectra of polycyclic aromatic hydrocarbons using machine learning methods.

Zenodo (CERN European Organization for Nuclear Research)
Lomonosov Moscow State University (RU)
Openalex Percentile: Top 64%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Towards Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS